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- Technical University of Munich
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international and dynamic team of researchers investigating problems in information retrieval (IR) and natural language processing (NLP) and their applications to health and medicine. The successful candidate
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This PhD position is part of the ErUM-Data project CmarT, which aims to develop a novel multi-scale imaging approach at synchrotron radiation facilities, particularly PETRA III at DESY. Biological
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reconstruction to overcome these challenges. Your tasks - develop physics-informed, self-supervised learning approaches for phase retrieval - implement reconstruction algorithms on HPC clusters for large-scale
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tomography and local adaptive reconstruction to overcome these challenges. Your tasks develop physics-informed, self-supervised learning approaches for phase retrieval implement reconstruction algorithms
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scientific discoveries explore and evaluate usability of system architectures such as Retrieval-Augmented Generation (RAG) for data retrieval and knowledge inference implementation of your machine learning
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evaluate usability of system architectures such as Retrieval-Augmented Generation (RAG) for data retrieval and knowledge inference - implementation of your machine learning pipeline in Python (using e.g
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TUD Dresden University of Technology, as a University of Excellence, is one of the leading and most dynamic research institutions in the country. Founded in 1828, today it is a globally oriented
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incorporating data on resource productivity, innovative extraction methods, and social acceptance. For this you will work together with project partners to retrieve real world techno-economic data as a foundation
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At the Leibniz Institute of Atmospheric Physics (IAP), a part-time position (75%) in the “Satellite Data Analysis” working group is available as PhD Student (f/m/d) The position is initially offered
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understand microbial life and the ways in which it affects our biosphere. MarMic students are trained to think globally and to choose from both holistic and reductionistic research approaches. The breadth